



import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY

@is_pipeline_test
class  snake_case__(unittest.TestCase     ):

					"""simple docstring"""






					lowercase_								=						MODEL_FOR_MASKED_LM_MAPPING
					lowercase_								=						TF_MODEL_FOR_MASKED_LM_MAPPING







					def 					snake_case	(				self   :      Optional[Any]	):
									super().tearDown()
									# clean-up as much as possible GPU memory occupied by PyTorch
									gc.collect()
									if is_torch_available():
													import torch

													torch.cuda.empty_cache()







					@require_tf
					def 					snake_case	(				self   :      Tuple	):
									lowercase__	:       List[str]						=	pipeline(task="fill-mask" ,  model="sshleifer/tiny-distilroberta-base" ,  top_k=2 ,  framework="tf"	)
									lowercase__	:       Dict						=	unmasker("My name is <mask>"	)
									self.assertEqual(
									    nested_simplify(lowerCAmelCase__ ,  decimals=6	) ,  [
									        {"sequence": "My name is grouped", "score": 2.1E-0_5, "token": 38_015, "token_str": " grouped"},
									        {"sequence": "My name is accuser", "score": 2.1E-0_5, "token": 25_506, "token_str": " accuser"},
									    ] ,  )

									lowercase__	:       Dict						=	unmasker("The largest city in France is <mask>"	)
									self.assertEqual(
									    nested_simplify(lowerCAmelCase__ ,  decimals=6	) ,  [
									        {
									            "sequence": "The largest city in France is grouped",
									            "score": 2.1E-0_5,
									            "token": 38_015,
									            "token_str": " grouped",
									        },
									        {
									            "sequence": "The largest city in France is accuser",
									            "score": 2.1E-0_5,
									            "token": 25_506,
									            "token_str": " accuser",
									        },
									    ] ,  )

									lowercase__	:       Dict						=	unmasker("My name is <mask>" ,  targets=[" Patrick", " Clara", " Teven"] ,  top_k=3	)
									self.assertEqual(
									    nested_simplify(lowerCAmelCase__ ,  decimals=6	) ,  [
									        {"sequence": "My name is Clara", "score": 2E-0_5, "token": 13_606, "token_str": " Clara"},
									        {"sequence": "My name is Patrick", "score": 2E-0_5, "token": 3_499, "token_str": " Patrick"},
									        {"sequence": "My name is Te", "score": 1.9E-0_5, "token": 2_941, "token_str": " Te"},
									    ] ,  )







					@require_torch
					def 					snake_case	(				self   :      List[str]	):
									lowercase__	:       str						=	pipeline(task="fill-mask" ,  model="sshleifer/tiny-distilroberta-base" ,  top_k=2 ,  framework="pt"	)

									lowercase__	:       Any						=	unmasker("My name is <mask>"	)
									self.assertEqual(
									    nested_simplify(lowerCAmelCase__ ,  decimals=6	) ,  [
									        {"sequence": "My name is Maul", "score": 2.2E-0_5, "token": 35_676, "token_str": " Maul"},
									        {"sequence": "My name isELS", "score": 2.2E-0_5, "token": 16_416, "token_str": "ELS"},
									    ] ,  )

									lowercase__	:       str						=	unmasker("The largest city in France is <mask>"	)
									self.assertEqual(
									    nested_simplify(lowerCAmelCase__ ,  decimals=6	) ,  [
									        {
									            "sequence": "The largest city in France is Maul",
									            "score": 2.2E-0_5,
									            "token": 35_676,
									            "token_str": " Maul",
									        },
									        {"sequence": "The largest city in France isELS", "score": 2.2E-0_5, "token": 16_416, "token_str": "ELS"},
									    ] ,  )

									lowercase__	:       List[Any]						=	unmasker("My name is <mask>" ,  targets=[" Patrick", " Clara", " Teven"] ,  top_k=3	)
									self.assertEqual(
									    nested_simplify(lowerCAmelCase__ ,  decimals=6	) ,  [
									        {"sequence": "My name is Patrick", "score": 2.1E-0_5, "token": 3_499, "token_str": " Patrick"},
									        {"sequence": "My name is Te", "score": 2E-0_5, "token": 2_941, "token_str": " Te"},
									        {"sequence": "My name is Clara", "score": 2E-0_5, "token": 13_606, "token_str": " Clara"},
									    ] ,  )

									lowercase__	:       int						=	unmasker("My name is <mask> <mask>" ,  top_k=2	)

									self.assertEqual(
									    nested_simplify(lowerCAmelCase__ ,  decimals=6	) ,  [
									        [
									            {
									                "score": 2.2E-0_5,
									                "token": 35_676,
									                "token_str": " Maul",
									                "sequence": "<s>My name is Maul<mask></s>",
									            },
									            {"score": 2.2E-0_5, "token": 16_416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
									        ],
									        [
									            {
									                "score": 2.2E-0_5,
									                "token": 35_676,
									                "token_str": " Maul",
									                "sequence": "<s>My name is<mask> Maul</s>",
									            },
									            {"score": 2.2E-0_5, "token": 16_416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
									        ],
									    ] ,  )







					@require_torch_gpu
					def 					snake_case	(				self   :      List[str]	):
									lowercase__	:       Optional[int]						=	pipeline("fill-mask" ,  model="hf-internal-testing/tiny-random-distilbert" ,  device=0 ,  framework="pt"	)

									# convert model to fp16
									pipe.model.half()

									lowercase__	:       str						=	pipe("Paris is the [MASK] of France."	)
									# We actually don't care about the result, we just want to make sure
									# it works, meaning the float16 tensor got casted back to float32
									# for postprocessing.
									self.assertIsInstance(lowerCAmelCase__ ,  lowerCAmelCase__	)







					@slow
					@require_torch
					def 					snake_case	(				self   :      Optional[int]	):
									lowercase__	:       int						=	pipeline(task="fill-mask" ,  model="distilroberta-base" ,  top_k=2 ,  framework="pt"	)
									self.run_large_test(lowerCAmelCase__	)







					@slow
					@require_tf
					def 					snake_case	(				self   :      List[str]	):
									lowercase__	:       List[str]						=	pipeline(task="fill-mask" ,  model="distilroberta-base" ,  top_k=2 ,  framework="tf"	)
									self.run_large_test(lowerCAmelCase__	)







					def 					snake_case	(				self   :      Dict ,  SCREAMING_SNAKE_CASE   :      Tuple	):
									lowercase__	:       Any						=	unmasker("My name is <mask>"	)
									self.assertEqual(
									    nested_simplify(lowerCAmelCase__	) ,  [
									        {"sequence": "My name is John", "score": 0.008, "token": 610, "token_str": " John"},
									        {"sequence": "My name is Chris", "score": 0.007, "token": 1_573, "token_str": " Chris"},
									    ] ,  )
									lowercase__	:       Dict						=	unmasker("The largest city in France is <mask>"	)
									self.assertEqual(
									    nested_simplify(lowerCAmelCase__	) ,  [
									        {
									            "sequence": "The largest city in France is Paris",
									            "score": 0.251,
									            "token": 2_201,
									            "token_str": " Paris",
									        },
									        {
									            "sequence": "The largest city in France is Lyon",
									            "score": 0.214,
									            "token": 12_790,
									            "token_str": " Lyon",
									        },
									    ] ,  )

									lowercase__	:       str						=	unmasker("My name is <mask>" ,  targets=[" Patrick", " Clara", " Teven"] ,  top_k=3	)
									self.assertEqual(
									    nested_simplify(lowerCAmelCase__	) ,  [
									        {"sequence": "My name is Patrick", "score": 0.005, "token": 3_499, "token_str": " Patrick"},
									        {"sequence": "My name is Clara", "score": 0.000, "token": 13_606, "token_str": " Clara"},
									        {"sequence": "My name is Te", "score": 0.000, "token": 2_941, "token_str": " Te"},
									    ] ,  )







					@require_torch
					def 					snake_case	(				self   :      List[Any]	):
									lowercase__	:       Tuple						=	pipeline(task="fill-mask" ,  model="sshleifer/tiny-distilroberta-base" ,  framework="pt"	)
									lowercase__	:       Dict						=	None
									lowercase__	:       Optional[Any]						=	None
									self.run_pipeline_test(lowerCAmelCase__ ,  []	)







					@require_tf
					def 					snake_case	(				self   :      Tuple	):
									lowercase__	:       List[Any]						=	pipeline(task="fill-mask" ,  model="sshleifer/tiny-distilroberta-base" ,  framework="tf"	)
									lowercase__	:       int						=	None
									lowercase__	:       List[Any]						=	None
									self.run_pipeline_test(lowerCAmelCase__ ,  []	)







					def 					snake_case	(				self   :      List[Any] ,  SCREAMING_SNAKE_CASE   :      List[Any] ,  SCREAMING_SNAKE_CASE   :      List[Any] ,  SCREAMING_SNAKE_CASE   :      Optional[int]	):


									if tokenizer is None or tokenizer.mask_token_id is None:
													self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)"	)

									lowercase__	:       Union[str, Any]						=	FillMaskPipeline(model=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__	)
									lowercase__	:       List[str]						=	[
									    f"""This is another {tokenizer.mask_token} test""",
									]
									return fill_masker, examples







					def 					snake_case	(				self   :      Optional[Any] ,  SCREAMING_SNAKE_CASE   :      Optional[Any] ,  SCREAMING_SNAKE_CASE   :      Dict	):
									lowercase__	:       Tuple						=	fill_masker.tokenizer
									lowercase__	:       List[Any]						=	fill_masker.model

									lowercase__	:       List[str]						=	fill_masker(
									    f"""This is a {tokenizer.mask_token}""" ,  )
									self.assertEqual(
									    lowerCAmelCase__ ,  [
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									    ] ,  )

									lowercase__	:       List[str]						=	fill_masker([f"""This is a {tokenizer.mask_token}"""]	)
									self.assertEqual(
									    lowerCAmelCase__ ,  [
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									    ] ,  )

									lowercase__	:       Union[str, Any]						=	fill_masker([f"""This is a {tokenizer.mask_token}""", f"""Another {tokenizer.mask_token} great test."""]	)
									self.assertEqual(
									    lowerCAmelCase__ ,  [
									        [
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        ],
									        [
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        ],
									    ] ,  )

									with self.assertRaises(lowerCAmelCase__	):
													fill_masker([None]	)
									# No mask_token is not supported
									with self.assertRaises(lowerCAmelCase__	):
													fill_masker("This is"	)

									self.run_test_top_k(lowerCAmelCase__ ,  lowerCAmelCase__	)
									self.run_test_targets(lowerCAmelCase__ ,  lowerCAmelCase__	)
									self.run_test_top_k_targets(lowerCAmelCase__ ,  lowerCAmelCase__	)
									self.fill_mask_with_duplicate_targets_and_top_k(lowerCAmelCase__ ,  lowerCAmelCase__	)
									self.fill_mask_with_multiple_masks(lowerCAmelCase__ ,  lowerCAmelCase__	)







					def 					snake_case	(				self   :      str ,  SCREAMING_SNAKE_CASE   :      Tuple ,  SCREAMING_SNAKE_CASE   :      Tuple	):
									lowercase__	:       Optional[int]						=	tokenizer.get_vocab()
									lowercase__	:       Optional[Any]						=	sorted(vocab.keys()	)[:2]
									# Pipeline argument
									lowercase__	:       Dict						=	FillMaskPipeline(model=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__ ,  targets=lowerCAmelCase__	)
									lowercase__	:       str						=	fill_masker(f"""This is a {tokenizer.mask_token}"""	)
									self.assertEqual(
									    lowerCAmelCase__ ,  [
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									    ] ,  )
									lowercase__	:       List[str]						=	{vocab[el] for el in targets}
									self.assertEqual({el["token"] for el in outputs} ,  lowerCAmelCase__	)
									lowercase__	:       Tuple						=	[tokenizer.decode([x]	) for x in target_ids]
									self.assertEqual({el["token_str"] for el in outputs} ,  set(lowerCAmelCase__	)	)

									# Call argument
									lowercase__	:       Union[str, Any]						=	FillMaskPipeline(model=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__	)
									lowercase__	:       List[str]						=	fill_masker(f"""This is a {tokenizer.mask_token}""" ,  targets=lowerCAmelCase__	)
									self.assertEqual(
									    lowerCAmelCase__ ,  [
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									    ] ,  )
									lowercase__	:       Optional[Any]						=	{vocab[el] for el in targets}
									self.assertEqual({el["token"] for el in outputs} ,  lowerCAmelCase__	)
									lowercase__	:       Optional[Any]						=	[tokenizer.decode([x]	) for x in target_ids]
									self.assertEqual({el["token_str"] for el in outputs} ,  set(lowerCAmelCase__	)	)

									# Score equivalence
									lowercase__	:       Any						=	fill_masker(f"""This is a {tokenizer.mask_token}""" ,  targets=lowerCAmelCase__	)
									lowercase__	:       Optional[int]						=	[top_mask["token_str"] for top_mask in outputs]
									lowercase__	:       int						=	[top_mask["score"] for top_mask in outputs]

									# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
									if set(lowerCAmelCase__	) == set(lowerCAmelCase__	):
													lowercase__	:       str						=	fill_masker(f"""This is a {tokenizer.mask_token}""" ,  targets=lowerCAmelCase__	)
													lowercase__	:       Optional[int]						=	[top_mask["score"] for top_mask in unmasked_targets]
													self.assertEqual(nested_simplify(lowerCAmelCase__	) ,  nested_simplify(lowerCAmelCase__	)	)

									# Raises with invalid
									with self.assertRaises(lowerCAmelCase__	):
													lowercase__	:       str						=	fill_masker(f"""This is a {tokenizer.mask_token}""" ,  targets=[]	)
									# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
									if "" not in tokenizer.get_vocab():
													with self.assertRaises(lowerCAmelCase__	):
																	lowercase__	:       str						=	fill_masker(f"""This is a {tokenizer.mask_token}""" ,  targets=[""]	)
													with self.assertRaises(lowerCAmelCase__	):
																	lowercase__	:       Any						=	fill_masker(f"""This is a {tokenizer.mask_token}""" ,  targets=""	)







					def 					snake_case	(				self   :      str ,  SCREAMING_SNAKE_CASE   :      Any ,  SCREAMING_SNAKE_CASE   :      Dict	):
									lowercase__	:       Dict						=	FillMaskPipeline(model=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__ ,  top_k=2	)
									lowercase__	:       Tuple						=	fill_masker(f"""This is a {tokenizer.mask_token}"""	)
									self.assertEqual(
									    lowerCAmelCase__ ,  [
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									    ] ,  )

									lowercase__	:       str						=	FillMaskPipeline(model=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__	)
									lowercase__	:       Any						=	fill_masker(f"""This is a {tokenizer.mask_token}""" ,  top_k=2	)
									self.assertEqual(
									    lowerCAmelCase__ ,  [
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									    ] ,  )
									self.assertEqual(nested_simplify(lowerCAmelCase__	) ,  nested_simplify(lowerCAmelCase__	)	)







					def 					snake_case	(				self   :      Union[str, Any] ,  SCREAMING_SNAKE_CASE   :      Dict ,  SCREAMING_SNAKE_CASE   :      Union[str, Any]	):
									lowercase__	:       List[Any]						=	tokenizer.get_vocab()
									lowercase__	:       Union[str, Any]						=	FillMaskPipeline(model=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__	)

									# top_k=2, ntargets=3
									lowercase__	:       Optional[int]						=	sorted(vocab.keys()	)[:3]
									lowercase__	:       Union[str, Any]						=	fill_masker(f"""This is a {tokenizer.mask_token}""" ,  top_k=2 ,  targets=lowerCAmelCase__	)

									# If we use the most probably targets, and filter differently, we should still
									# have the same results
									lowercase__	:       str						=	[el["token_str"] for el in sorted(lowerCAmelCase__ ,  key=lambda SCREAMING_SNAKE_CASE	: x["score"] ,  reverse=lowerCAmelCase__	)]
									# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
									if set(lowerCAmelCase__	).issubset(lowerCAmelCase__	):
													lowercase__	:       Dict						=	fill_masker(f"""This is a {tokenizer.mask_token}""" ,  top_k=3 ,  targets=lowerCAmelCase__	)
													# They should yield exactly the same result
													self.assertEqual(nested_simplify(lowerCAmelCase__	) ,  nested_simplify(lowerCAmelCase__	)	)







					def 					snake_case	(				self   :      List[str] ,  SCREAMING_SNAKE_CASE   :      List[Any] ,  SCREAMING_SNAKE_CASE   :      List[Any]	):
									lowercase__	:       List[str]						=	FillMaskPipeline(model=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__	)
									lowercase__	:       Union[str, Any]						=	tokenizer.get_vocab()
									# String duplicates + id duplicates
									lowercase__	:       int						=	sorted(vocab.keys()	)[:3]
									lowercase__	:       Optional[Any]						=	[targets[0], targets[1], targets[0], targets[2], targets[1]]
									lowercase__	:       Union[str, Any]						=	fill_masker(f"""My name is {tokenizer.mask_token}""" ,  targets=lowerCAmelCase__ ,  top_k=10	)

									# The target list contains duplicates, so we can't output more
									# than them
									self.assertEqual(len(lowerCAmelCase__	) ,  3	)







					def 					snake_case	(				self   :      str ,  SCREAMING_SNAKE_CASE   :      int ,  SCREAMING_SNAKE_CASE   :      Dict	):
									lowercase__	:       Union[str, Any]						=	FillMaskPipeline(model=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__	)

									lowercase__	:       Optional[int]						=	fill_masker(
									    f"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}""" ,  top_k=2	)
									self.assertEqual(
									    lowerCAmelCase__ ,  [
									        [
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        ],
									        [
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        ],
									        [
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									            {"sequence": ANY(lowerCAmelCase__	), "score": ANY(lowerCAmelCase__	), "token": ANY(lowerCAmelCase__	), "token_str": ANY(lowerCAmelCase__	)},
									        ],
									    ] ,  )





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a    :	Union[str, Any]         =    1_6
__a    :	int         =    3_2





def 							UpperCAmelCase		(       lowercase				,     lowercase = 16				):







				"""simple docstring"""



				__lowercase					     =				AutoTokenizer.from_pretrained('''bert-base-cased'''				)
				__lowercase					     =				load_dataset('''glue'''				,     '''mrpc'''				)

				def tokenize_function(lowercase				):
								# max_length=None => use the model max length (it's actually the default)
								__lowercase					     =				tokenizer(examples['''sentence1''']				,     examples['''sentence2''']				,     truncation=lowercase				,     max_length=lowercase				)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								__lowercase					     =				datasets.map(
								    lowercase				,     batched=lowercase				,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,     )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__lowercase					     =				tokenized_datasets.rename_column('''label'''				,     '''labels'''				)

				def collate_fn(lowercase				):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__lowercase					     =				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__lowercase					     =				16
								elif accelerator.mixed_precision != "no":
												__lowercase					     =				8
								else:
												__lowercase					     =				None

								return tokenizer.pad(
								    lowercase				,     padding='''longest'''				,     max_length=lowercase				,     pad_to_multiple_of=lowercase				,     return_tensors='''pt'''				,     )

				# Instantiate dataloaders.
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''train''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''validation''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		__a    :	List[Any]         =    mocked_dataloaders  # noqa: F811





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,     lowercase				) == "1":
								__lowercase					     =				2
				# Initialize accelerator
				__lowercase					     =				Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__lowercase					     =				config['''lr''']
				__lowercase					     =				int(config['''num_epochs''']				)
				__lowercase					     =				int(config['''seed''']				)
				__lowercase					     =				int(config['''batch_size''']				)

				__lowercase					     =				evaluate.load('''glue'''				,     '''mrpc'''				)

				# If the batch size is too big we use gradient accumulation
				__lowercase					     =				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__lowercase					     =				batch_size // MAX_GPU_BATCH_SIZE
								__lowercase					     =				MAX_GPU_BATCH_SIZE

				set_seed(lowercase				)
				__lowercase     ,	__lowercase					     =				get_dataloaders(lowercase				,     lowercase				)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__lowercase					     =				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,     return_dict=lowercase				)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				__lowercase					     =				model.to(accelerator.device				)

				# Instantiate optimizer
				__lowercase					     =				AdamW(params=model.parameters()				,     lr=lowercase				)

				# Instantiate scheduler
				__lowercase					     =				get_linear_schedule_with_warmup(
				    optimizer=lowercase				,     num_warmup_steps=100				,     num_training_steps=(len(lowercase				) * num_epochs) // gradient_accumulation_steps				,     )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase					     =				accelerator.prepare(
				    lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				)

				# Now we train the model
				for epoch in range(lowercase				):
								model.train()
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.loss
												__lowercase					     =				loss / gradient_accumulation_steps
												accelerator.backward(lowercase				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								__lowercase					     =				0
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												with torch.no_grad():
																__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.logits.argmax(dim=-1				)
												__lowercase     ,	__lowercase					     =				accelerator.gather((predictions, batch['''labels'''])				)
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(lowercase				) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				__lowercase					     =				predictions[: len(eval_dataloader.dataset				) - samples_seen]
																				__lowercase					     =				references[: len(eval_dataloader.dataset				) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=lowercase				,     references=lowercase				,     )

								__lowercase					     =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"				,     lowercase				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				argparse.ArgumentParser(description='''Simple example of training script.'''				)
				parser.add_argument(
				    '''--mixed_precision'''				,     type=lowercase				,     default=lowercase				,     choices=['''no''', '''fp16''', '''bf16''', '''fp8''']				,     help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''				,     )
				parser.add_argument('''--cpu'''				,     action='''store_true'''				,     help='''If passed, will train on the CPU.'''				)
				__lowercase					     =				parser.parse_args()
				__lowercase					     =				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase				,     lowercase				)


if __name__ == "__main__":
		main()